Bibliographic record
Abstract
The question of bank competition is vitally important for a number of reasons. The essential role of bank credit and other financial services as an input in the production of most other goods and services places banks in a unique and influential position, such that any allocative inefficiency or other market distortions in banking are almost certain to be felt throughout the economy. Moreover, recent history has provided numerous instances where the textbook paradigm of atomistic competition has proven inadequate as a policy guide for efficient banking—either because there are simply too few banks to rely on sheer numbers as a guarantee of vigorous competition, as in Canada; or because of evidence that there can be such a thing as “too much competition” in banking, as suggested by several studies; or because of instances where nearly competitive pricing has been observed in markets containing only one or two banks; or because, conversely, substantially noncompetitive pricing has sometimes been deduced in banking products—such as credit cards— with thousands of suppliers. Thus, the study of bank competition remains an urgent field of research. Claessens and Laeven (2004, this issue of JMCB) make two key contributions here. First, they extend a proven empirical method to an unprecedentedly large and varied crosscountry sample. Second, they offer the logical and policy-relevant additional step of seeking to identify factors associated with variations in measured conduct. This step is needed to assess the empirical validity of the traditional structure-conductperformance paradigm in their sample and is especially important where that paradigm is found lacking as a predictor of bank conduct. Indeed, although the authors
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.032 | 0.026 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".